Enhancing Intrusion Detection in Wireless Sensor Networks through Deep Hybrid Network Empowered by SC-Attention Mechanism

Abstract

Abstract WSNs are often deployed in unattended or hostile environments, making them vulnerable to various types of attacks. Ensuring the security of WSNs is crucial, especially if the data being monitored is sensitive or critical. An intrusion detection system (IDS) can help detect unauthorized access or malicious activities within the network. In the field of network intrusion detection systems (NIDS), traditional approaches face limitations in effectively detecting evolving threats and unknown attack patterns. To overcome these challenges, this research proposes a novel approach called the Deep Hybrid Network with spatial and channel attention (DHN-SCA) that integrates deep learning techniques with attention mechanisms. The DHN combines convolutional neural networks (CNNs) with a Local Attention Module to enhance the accuracy and efficiency of intrusion detection. The Local Attention Module consists of two sub-modules: Spatial Attention and Channel Attention. Spatial Attention applies average pooling to the feature tensor, while Channel Attention incorporates global average pooling and global max pooling followed by fully connected layers. These sub-modules refine the feature tensor through element-wise multiplication operations with the original features. Experiments and evaluations are conducted on benchmark datasets to assess the performance of the DHN. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to compare the DHN's effectiveness with existing intrusion detection approaches.